Predicting critical transitions in assortative spin-shifting networks
Methods to forecast critical transitions, i.e. abrupt changes in systems’ equilibrium states have relevance in scientific fields such as ecology, seismology, finance and medicine among others. So far, the bulk of investigations on forecasting methods builds on equation-based modeling methods, which...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934323/?tool=EBI |
| _version_ | 1828012274991759360 |
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| author | Manfred Füllsack Daniel Reisinger Raven Adam Marie Kapeller Georg Jäger |
| author_facet | Manfred Füllsack Daniel Reisinger Raven Adam Marie Kapeller Georg Jäger |
| author_sort | Manfred Füllsack |
| collection | DOAJ |
| description | Methods to forecast critical transitions, i.e. abrupt changes in systems’ equilibrium states have relevance in scientific fields such as ecology, seismology, finance and medicine among others. So far, the bulk of investigations on forecasting methods builds on equation-based modeling methods, which consider system states as aggregates and thus do not account for the different connection strengths in each part of the system. This seems inadequate against the background of studies that insinuate that critical transitions can originate in sparsely connected parts of systems. Here we use agent-based spin-shifting models with assortative network representations to distinguish different interaction densities. Our investigations confirm that signals of imminent critical transitions can indeed be detected significantly earlier in network parts with low link degrees. We discuss the reason for this circumstance on the basis of the free energy principle. |
| first_indexed | 2024-04-10T09:29:46Z |
| format | Article |
| id | doaj.art-4e682e7733b1420281ad47be2bcf441d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T09:29:46Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4e682e7733b1420281ad47be2bcf441d2023-02-19T05:31:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01182Predicting critical transitions in assortative spin-shifting networksManfred FüllsackDaniel ReisingerRaven AdamMarie KapellerGeorg JägerMethods to forecast critical transitions, i.e. abrupt changes in systems’ equilibrium states have relevance in scientific fields such as ecology, seismology, finance and medicine among others. So far, the bulk of investigations on forecasting methods builds on equation-based modeling methods, which consider system states as aggregates and thus do not account for the different connection strengths in each part of the system. This seems inadequate against the background of studies that insinuate that critical transitions can originate in sparsely connected parts of systems. Here we use agent-based spin-shifting models with assortative network representations to distinguish different interaction densities. Our investigations confirm that signals of imminent critical transitions can indeed be detected significantly earlier in network parts with low link degrees. We discuss the reason for this circumstance on the basis of the free energy principle.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934323/?tool=EBI |
| spellingShingle | Manfred Füllsack Daniel Reisinger Raven Adam Marie Kapeller Georg Jäger Predicting critical transitions in assortative spin-shifting networks PLoS ONE |
| title | Predicting critical transitions in assortative spin-shifting networks |
| title_full | Predicting critical transitions in assortative spin-shifting networks |
| title_fullStr | Predicting critical transitions in assortative spin-shifting networks |
| title_full_unstemmed | Predicting critical transitions in assortative spin-shifting networks |
| title_short | Predicting critical transitions in assortative spin-shifting networks |
| title_sort | predicting critical transitions in assortative spin shifting networks |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934323/?tool=EBI |
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